Figures 3
rstudio
September 11, 2024 14:09:51 CEST
Introduction
Load packages
Open corrected counts and annotated Seurat object
Load the corrected (SoupX), normalized
(SCTransformed) and annotated (Twice mapped - Tabula
Sapiens Skin reference and PBMC azimuth reference) data.
srat <- readRDS(params$path_to_data)
meta <- srat@meta.data
meta$WHO <- "SD"
meta$WHO[meta$patient %in% c("NeoBCC007_post", "NeoBCC008_post", "NeoBCC012_post", "NeoBCC017_post")] <- "CR"
meta$WHO[meta$patient %in% c("NeoBCC004_post", "NeoBCC006_post", "NeoBCC010_post", "NeoBCC011_post")] <- "PR"
srat <- AddMetaData(srat, meta$WHO, col.name = "WHO")
srat$WHO <- factor(srat$WHO, levels = c("CR", "PR", "SD"))Define levels and colors
srat@meta.data$anno_l1 <- factor(srat@meta.data$anno_l1, levels=c("other",
"Mast cells",
"Mono-Mac",
"LC",
"DC",
"pDC",
"Plasma cells",
"B cells" ,
"Proliferating cells",
"Natural killer cells",
"CD8+ T cells",
"Tregs",
"CD4+ T cells" ,
"Melanocytes",
"Endothelial cells",
"Fibroblasts",
"Keratinocytes",
"Malignant cells"))
colors <- c("Malignant cells" = "#bd0026",
"Keratinocytes" = "#dfc27d",
"Fibroblasts" = "#f6e8c3",
"Endothelial cells" = "#54278f",
"Melanocytes" = "#a65628",
"CD4+ T cells" = "#b8e186",
"Tregs" = "#ae017e",
"CD8+ T cells" = "#fbb4ae",
"Proliferating cells" = "#b3cde3",
"Natural killer cells" = "#9e9ac8",
"B cells" = "#7bccc4",
"Plasma cells" = "#35978f",
"pDC" = "#fe9929",
"DC" = "#e7298a",
"LC" = "yellow" ,
"Mono-Mac" = "#fec44f",
"Mast cells" = "#bf812d",
"other" = "#bdbdbd")Figure 3a
p <- SCpubr::do_DimPlot(sample = srat,
colors.use = colors,
group.by = "anno_l1",
pt.size=0.5, label = TRUE,
repel = TRUE,
legend.position = "none",
label.color = "black") +
theme_minimal() +
NoLegend() +
theme(text = element_text(size=20))
p
Figure 3b
genes <- list(
"Mal." = c("KRT17", "EPCAM", "BCAM"),
"Kerati." = c("FGFBP1", "KRT1", "KRT6A"),
"Fibro." = c("COL1A1", "COL1A2", "COL6A2"),
"E" = c("VWF"),
"Mel" = c("MLANA", "PMEL"),
"CD4+T" = c("CD3E","CD2", "CD4" ),
"Tregs" = c("IL2RA", "CD25", "FOXP3", "TNFRSF4"),
"CD8+T" = c("CD8A", "GZMA"),
"NK" = c( "KLRC1", "PRF1", "GNLY"),
"P" = c("MKI67"),
"B" = c("MS4A1", "CD19"),
"Plasma" = c("IGKC", "CD38", "SDC1"),
"pDC" = c( "IRF8", "CLEC4C"),
"DC" = c("LAMP3", "CCR7"),
"LC" = c("CD1A", "CD207"),
"Mono-Mac" = c("CD68", "CD14" ),
"Mast" = c("KIT", "SOCS1"))
p <- SCpubr::do_DotPlot(sample = srat,
features = genes,
group.by = "anno_l1",
font.size = 25,
legend.length = 4,
legend.type = "colorbar",
dot.scale = 8,
sequential.palette ="PiYG",
scale = TRUE,
sequential.direction = -1)
p
Session Info
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Vienna
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stringr_1.5.1 msigdbr_7.5.1 DOSE_3.26.2 org.Hs.eg.db_3.17.0
## [5] AnnotationDbi_1.62.2 IRanges_2.34.1 S4Vectors_0.38.2 Biobase_2.60.0
## [9] BiocGenerics_0.46.0 clusterProfiler_4.8.3 enrichplot_1.20.3 scales_1.3.0
## [13] RColorBrewer_1.1-3 ggnewscale_0.4.10 tidyr_1.3.1 scRepertoire_1.10.1
## [17] dittoSeq_1.12.2 canceRbits_0.1.6 ggpubr_0.6.0.999 ggplot2_3.5.1
## [21] viridis_0.6.5 viridisLite_0.4.2 reshape2_1.4.4 tibble_3.2.1
## [25] SCpubr_2.0.2 DT_0.32 patchwork_1.2.0 dplyr_1.1.4
## [29] Seurat_5.0.3 SeuratObject_5.0.1 sp_2.1-3
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.4 matrixStats_1.2.0 spatstat.sparse_3.0-3
## [4] bitops_1.0-7 HDO.db_0.99.1 httr_1.4.7
## [7] doParallel_1.0.17 tools_4.3.0 sctransform_0.4.1
## [10] backports_1.4.1 utf8_1.2.4 R6_2.5.1
## [13] vegan_2.6-4 lazyeval_0.2.2 uwot_0.1.16
## [16] mgcv_1.9-1 permute_0.9-7 withr_3.0.0
## [19] gridExtra_2.3 progressr_0.14.0 cli_3.6.2
## [22] spatstat.explore_3.2-7 fastDummies_1.7.3 scatterpie_0.2.1
## [25] isoband_0.2.7 labeling_0.4.3 sass_0.4.9
## [28] spatstat.data_3.0-4 ggridges_0.5.6 pbapply_1.7-2
## [31] yulab.utils_0.1.4 gson_0.1.0 stringdist_0.9.12
## [34] parallelly_1.37.1 limma_3.56.2 RSQLite_2.3.5
## [37] VGAM_1.1-10 rstudioapi_0.16.0 generics_0.1.3
## [40] gridGraphics_0.5-1 ica_1.0-3 spatstat.random_3.2-3
## [43] crosstalk_1.2.1 car_3.1-2 GO.db_3.17.0
## [46] Matrix_1.6-5 ggbeeswarm_0.7.2 fansi_1.0.6
## [49] abind_1.4-5 lifecycle_1.0.4 edgeR_3.42.4
## [52] yaml_2.3.8 carData_3.0-5 SummarizedExperiment_1.30.2
## [55] qvalue_2.32.0 Rtsne_0.17 blob_1.2.4
## [58] grid_4.3.0 promises_1.2.1 crayon_1.5.2
## [61] miniUI_0.1.1.1 lattice_0.22-6 cowplot_1.1.3
## [64] KEGGREST_1.40.1 pillar_1.9.0 knitr_1.45
## [67] fgsea_1.26.0 GenomicRanges_1.52.1 future.apply_1.11.1
## [70] codetools_0.2-19 fastmatch_1.1-4 leiden_0.4.3.1
## [73] glue_1.7.0 downloader_0.4 ggfun_0.1.5
## [76] data.table_1.15.2 treeio_1.24.3 vctrs_0.6.5
## [79] png_0.1-8 spam_2.10-0 gtable_0.3.5
## [82] assertthat_0.2.1 cachem_1.1.0 xfun_0.43
## [85] S4Arrays_1.0.6 mime_0.12 tidygraph_1.3.1
## [88] survival_3.5-8 DElegate_1.2.1 SingleCellExperiment_1.22.0
## [91] pheatmap_1.0.12 iterators_1.0.14 fitdistrplus_1.1-11
## [94] ROCR_1.0-11 nlme_3.1-164 ggtree_3.13.0.001
## [97] bit64_4.0.5 RcppAnnoy_0.0.22 evd_2.3-6.1
## [100] GenomeInfoDb_1.36.4 bslib_0.6.2 irlba_2.3.5.1
## [103] vipor_0.4.7 KernSmooth_2.23-22 DBI_1.2.2
## [106] colorspace_2.1-0 ggrastr_1.0.2 tidyselect_1.2.1
## [109] bit_4.0.5 compiler_4.3.0 SparseM_1.81
## [112] DelayedArray_0.26.7 plotly_4.10.4 shadowtext_0.1.3
## [115] lmtest_0.9-40 digest_0.6.35 goftest_1.2-3
## [118] spatstat.utils_3.0-4 rmarkdown_2.26 XVector_0.40.0
## [121] htmltools_0.5.8 pkgconfig_2.0.3 sparseMatrixStats_1.12.2
## [124] MatrixGenerics_1.12.3 highr_0.10 fastmap_1.2.0
## [127] rlang_1.1.4 htmlwidgets_1.6.4 shiny_1.8.1
## [130] farver_2.1.2 jquerylib_0.1.4 zoo_1.8-12
## [133] jsonlite_1.8.8 BiocParallel_1.34.2 GOSemSim_2.26.1
## [136] RCurl_1.98-1.14 magrittr_2.0.3 GenomeInfoDbData_1.2.10
## [139] ggplotify_0.1.2 dotCall64_1.1-1 munsell_0.5.1
## [142] Rcpp_1.0.12 evmix_2.12 babelgene_22.9
## [145] ape_5.8 reticulate_1.35.0 truncdist_1.0-2
## [148] stringi_1.8.4 ggalluvial_0.12.5 ggraph_2.2.1
## [151] zlibbioc_1.46.0 MASS_7.3-60.0.1 plyr_1.8.9
## [154] parallel_4.3.0 listenv_0.9.1 ggrepel_0.9.5
## [157] forcats_1.0.0 deldir_2.0-4 Biostrings_2.68.1
## [160] graphlayouts_1.1.1 splines_4.3.0 tensor_1.5
## [163] locfit_1.5-9.9 igraph_2.0.3 spatstat.geom_3.2-9
## [166] cubature_2.1.0 ggsignif_0.6.4 RcppHNSW_0.6.0
## [169] evaluate_0.23 foreach_1.5.2 tweenr_2.0.3
## [172] httpuv_1.6.15 RANN_2.6.1 purrr_1.0.2
## [175] polyclip_1.10-6 future_1.33.2 scattermore_1.2
## [178] ggforce_0.4.2 broom_1.0.5 xtable_1.8-4
## [181] tidytree_0.4.6 RSpectra_0.16-1 rstatix_0.7.2
## [184] later_1.3.2 gsl_2.1-8 aplot_0.2.3
## [187] beeswarm_0.4.0 memoise_2.0.1 cluster_2.1.6
## [190] powerTCR_1.20.0 globals_0.16.3